What is the difference between functions in math and functions in programming?

Question

What is the difference between functions in math and functions in programming?

Answer 1

In functional programming you have Referential Transparency, which means that you can replace a function with its value without altering the program. This is true in Math too, but this is not always true in Imperative languages.

A math function is defined by: a relationship that maps elements from one set (A) to another (B), mapping each element of the first set with only one of the other set. In C (as in other programming languages) this is also true, you have your input set, and your output set (which is almost always only ONE).

The main difference, is, then, that if you call f(x) in math, you will ALWAYS get the same answer, but if you call f'(x) in C, the answer may not be the same - the same arguments don't always return the same output. A function in non-functional languages may not depend solely on the arguments you give them, but on other things in the program (enclosing scope, globals, builtins, state if you're using object orientation).

Another difference between math and C functions, is that in Math you can't make a function that goes from a non-empty set to an empty set (in C this would be: You aren't requiered to always return something with your function). Also, not all function are computable (I don't know if there's something similiar to this in math..). You don't have functions for infinite sets (you have finite memory, so the set of the possible input parameters must be finite), but in math, you can define a function for infinite sets (like f: N -> N) and for uncountable sets (like f: R -> R) (In C you have floating point numbers, but they only represent a reduced set of real numbers, which is finite).

Summarizing:

In C you don't always have Referential Transparency. Your functions may not always give the same output for the same input parameters. You can have Math functions that defined for an infinite set of inputs, but in C functions your input is finite. In C functions you can have functions that returns nothing, but in Math you can't have that (if you have a function that has a non empty input set, you must map each element with one of another set).

Answer 2

It depends on the domain (I don't mean the domain of the function, I mean the domain of study) and also possibly the language.

In math, a function has an input that maps to only one output for a given input value (vertical line test, remember). In programming, this might not be strictly the same, depending on where you draw the line between "input" and "function logic."

For instance, let's imagine we have a function rand() that reads atmospheric conditions to arrive at a truly random number. Let's also imagine that a calling function takes one integer parameter as a mutiplier of sorts. Is the following a function?:

int giveRandAtmosWithMul(int mult)
{
    return mult * rand();
}

In the mathematic sense, it probably is not a function if you consider mult as the only input to the problem, but clearly rand() is offering input as well (even though rand() always has the same entry point in machine code).

As you can see, the differences aren't really objectively definable without some standard protocol that everybody agrees to.

Answer 3

I think the most important distinction is that functions in math (and functional programming) can't change state, while (imperative) programming functions can.

Answer 4

Other answers are correct - these are two different things. I'll show, on the contrary, they are related. I'll denote programming functions by ->, mathematical functions by =>.

Suppose you have a language that supports exceptions. In that case, you can think of a programming function A -> B as a mathematical function A => B + E where "B + E" means either something of type B, or something of type E. Your language has two keywords to return from a function, "return" and "throw".

Now, you can compose two functions f: A -> B and g: B -> C by writing g(f(x)). This is a function that takes an A and returns C. You can do that in many languages, like Java or Python. Under the hood, if f(x) throws an exception, g is not called and the exception is propagated - think about g(1/0). The language takes care of that and you don't need to check if the result of f is an exception. The way to describe that mathematically is although f: A => B+E and g: B => C+E, the language "lifts" the function g into B+E => C+E. g is now a function that might take an exception, but it will simply propagate it.

In other words define g': B+E => C+E by

        /   g(x)   if x ∈ B
g'(x)= |
        \   x      if x ∈ E

Having two functions f: A => B+E and g': B+E => C+E you can safely compose them in mathematical sense. And this is what g(f(x)) in programming does, but it has to lift g into g' first.

Think about nondeterminism. If you have a function A -> B that is nondeterministic, then you can describe it mathematically saying this is a function A => Set(B) where Set(B) is the set of possible results. For example, if f(1) might give you 1, 2 or 3, then mathematically f(1) = {1,2,3} although while programming when asked for f(1) you'll get only one of these numbers. Now you can compose nondeterministic functions A->B and B->C by writing g(f(x)). The result will be of type C, but it will be nondeterministic. Mathematically, composing functions A => Set(B) and B => Set(C) gives you A => Set(C). Although g takes only one value of B, you have to lift it to nondeterministic values g': Set(B) => Set(C). For example, g'({1,2}) is union of sets g(1) and g(2). So g(f(x)) means you run f, take set of all possible results and run g on each of them. There are two layers of nondeterminism, but they are "flattened" into one.

Same with global state. If you make every global variable as an argument of a function and the result, you can think there are no global variables, every function takes all global state and the language has to hand over the state when composing. A function A -> B reading and possibly writing state S is a function (A,S) => (B,S), which can also be written as A => (S => (B,S)). Writing this formally is more complicated, but it's the same pattern.

Same can be done with input/output, I described that in another SO answer.

The "magic" that allows to compose "effectful" functions is:

• A way to make the type effectful. For example, turn B into B+E or Set(B). I'll denote effectful version of type X as F(X).
• A way to lift the functions: B -> F(C) into F(B) -> F(C). It allows to compose functions A -> F(B) and B -> F(C).
• The keyword return must turn an ordinary value A into A+E, or singleton Set(A). So it's type must be X -> F(X).

A structure composed of those three is called a monad. Monads allow to describe those side effects . A monad might also have some specific functions, for example the exception monad has throw, the nondeterminism monad has fork, the state monad has get/put, the IO monad has read/write etc.

The moral is: If you regard special effects like randomizing, exceptions, nondeterminism, input/output as a part of the result of a function, then every function is referentially transparent and functions in imperative programming are really mathematical functions, but with very strange result types that also describe special effects. This is the approach taken by pure functional languages like Haskell.

Answer 5

Mathematical functions are declarative in nature i.e. they always have "what is" descriptions whereas functions in computer science are imperative i.e. they have "how to" descriptions.

Reference: Structure and interpretation of computer programs.

Answer 6

In math, functions don't throw exceptions. :)

A function in computer science is a chunk of code that takes inputs, does something and possibly returns outputs, but it can do a host of things between. It can fetch webpages, send emails, play video, whatever.

In math a function is something very specific and nothing else. A function is usually described as a "machine" that takes in inputs and spits out outputs. While computer science functions do take in inputs, and spit out outputs, they don't have to do so with the precise "the same input always yields the same output" that math requires (e.g. bool IsMyApplicationRunningInFullScreen() returns various values with no inputs at all).